{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "TRAIN_DATA_PATH = \"./data/Metro_train/\"\n",
    "TEST_DATA_PATH = \"./data/Metro_testA/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['time', 'lineID', 'stationID', 'deviceID', 'status', 'userID',\n",
      "       'payType'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "train,test = load_data()\n",
    "datestart = datetime.datetime.strptime(\"2019-01-01\", \"%Y-%m-%d\")\n",
    "date_list = []\n",
    "for i in range(25):\n",
    "    date_list.append(str(datestart)[0:10])\n",
    "    datestart += datetime.timedelta(days=+1)\n",
    "date_list.append(\"2019-01-28\")\n",
    "inout_train = pd.DataFrame()\n",
    "for record_date in date_list:\n",
    "    if record_date == \"2019-01-28\":\n",
    "        recoreds = pd.read_csv(TEST_DATA_PATH + f'testA_record_{record_date}.csv', encoding=\"utf-8\")\n",
    "    else:\n",
    "        recoreds = pd.read_csv(TRAIN_DATA_PATH + f'record_{record_date}.csv', encoding=\"utf-8\")\n",
    "    print(recoreds.columns)\n",
    "    break;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2539592 entries, 0 to 2539591\n",
      "Data columns (total 7 columns):\n",
      "time         object\n",
      "lineID       object\n",
      "stationID    int64\n",
      "deviceID     int64\n",
      "status       int64\n",
      "userID       object\n",
      "payType      int64\n",
      "dtypes: int64(4), object(3)\n",
      "memory usage: 135.6+ MB\n"
     ]
    }
   ],
   "source": [
    "recoreds.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>lineID</th>\n",
       "      <th>stationID</th>\n",
       "      <th>deviceID</th>\n",
       "      <th>status</th>\n",
       "      <th>userID</th>\n",
       "      <th>payType</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01 02:00:05</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 02:01:40</td>\n",
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       "      <td>2019-01-01 02:01:53</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-01 02:02:38</td>\n",
       "      <td>B</td>\n",
       "      <td>5</td>\n",
       "      <td>235</td>\n",
       "      <td>0</td>\n",
       "      <td>D9a337d37d9512184b8e3fd477934b293</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-01 02:03:42</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>1198</td>\n",
       "      <td>0</td>\n",
       "      <td>Dd1cde61886c23fdb7ef1fdb76c9b1234</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-01-01 02:04:11</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>109</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <th>6</th>\n",
       "      <td>2019-01-01 02:04:45</td>\n",
       "      <td>B</td>\n",
       "      <td>27</td>\n",
       "      <td>1354</td>\n",
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       "      <th>7</th>\n",
       "      <td>2019-01-01 02:05:02</td>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>D3951ac47cb9911c7adca0f23a9eb9bf2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-01-01 02:05:20</td>\n",
       "      <td>B</td>\n",
       "      <td>23</td>\n",
       "      <td>1198</td>\n",
       "      <td>0</td>\n",
       "      <td>Db5682ca9db937733e95db27b959e62a1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-01-01 02:05:34</td>\n",
       "      <td>B</td>\n",
       "      <td>20</td>\n",
       "      <td>1036</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time lineID  stationID  deviceID  status  \\\n",
       "0  2019-01-01 02:00:05      B         27      1354       0   \n",
       "1  2019-01-01 02:01:40      B          5       200       1   \n",
       "2  2019-01-01 02:01:53      B          5       247       0   \n",
       "3  2019-01-01 02:02:38      B          5       235       0   \n",
       "4  2019-01-01 02:03:42      B         23      1198       0   \n",
       "5  2019-01-01 02:04:11      B          3       109       0   \n",
       "6  2019-01-01 02:04:45      B         27      1354       1   \n",
       "7  2019-01-01 02:05:02      B          4       141       0   \n",
       "8  2019-01-01 02:05:20      B         23      1198       0   \n",
       "9  2019-01-01 02:05:34      B         20      1036       1   \n",
       "\n",
       "                              userID  payType  \n",
       "0  D13f76f42c9a677c4add94d9e480fb5c5        3  \n",
       "1  D9a337d37d9512184b8e3fd477934b293        3  \n",
       "2  Dc9e179298617f40b782490c1f3e2346c        3  \n",
       "3  D9a337d37d9512184b8e3fd477934b293        3  \n",
       "4  Dd1cde61886c23fdb7ef1fdb76c9b1234        3  \n",
       "5  D7a8fc96cbc11e5df389aa45a62870a48        3  \n",
       "6  D13f76f42c9a677c4add94d9e480fb5c5        3  \n",
       "7  D3951ac47cb9911c7adca0f23a9eb9bf2        3  \n",
       "8  Db5682ca9db937733e95db27b959e62a1        3  \n",
       "9  D01d6584e23de32f8225c756f4c3add76        3  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recoreds.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stationID</th>\n",
       "      <th>startTime</th>\n",
       "      <th>endTime</th>\n",
       "      <th>inNums</th>\n",
       "      <th>outNums</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01 05:30:00</td>\n",
       "      <td>2019-01-01 05:40:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>05:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01 05:40:00</td>\n",
       "      <td>2019-01-01 05:50:00</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>05:40:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01 05:50:00</td>\n",
       "      <td>2019-01-01 06:00:00</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>05:50:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01 06:00:00</td>\n",
       "      <td>2019-01-01 06:10:00</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>06:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01 06:10:00</td>\n",
       "      <td>2019-01-01 06:20:00</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>06:10:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stationID            startTime              endTime  inNums  outNums  \\\n",
       "0          0  2019-01-01 05:30:00  2019-01-01 05:40:00       1        0   \n",
       "1          0  2019-01-01 05:40:00  2019-01-01 05:50:00       4        0   \n",
       "2          0  2019-01-01 05:50:00  2019-01-01 06:00:00       3        1   \n",
       "3          0  2019-01-01 06:00:00  2019-01-01 06:10:00      17        0   \n",
       "4          0  2019-01-01 06:10:00  2019-01-01 06:20:00      21        0   \n",
       "\n",
       "         date      time  \n",
       "0  2019-01-01  05:30:00  \n",
       "1  2019-01-01  05:40:00  \n",
       "2  2019-01-01  05:50:00  \n",
       "3  2019-01-01  06:00:00  \n",
       "4  2019-01-01  06:10:00  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看每个日期的出行情况\n",
    "train = pd.read_csv(TEMP_DATA_PATH+\"inout_train.csv\", encoding=\"utf-8\")\n",
    "train['date'] = train.apply(lambda row: row['startTime'].split(' ')[0], axis=1)\n",
    "train['time'] = train.apply(lambda row: row['startTime'].split(' ')[1], axis=1)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13f83c5cf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "plt.plot(train[train['date']=='2019-01-21']['time'], train[train['date']=='2019-01-21']['inNums'])\n",
    "plt.plot(train[train['date']=='2019-01-28']['time'], train[train['date']=='2019-01-28']['inNums'],color=\"r\")\n",
    "#plt.plot(train[train['date']=='2019-01-15']['time'], train[train['date']=='2019-01-15']['inNums'],color=\"b\")\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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